Language models are few-shot learners

SetFit: Efficient Few-Shot Learning Without Prompts

Few-shot learning in practice: GPT-Neo and the 🤗 Accelerated Inference API

How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs

The paper introduces ramework, a blackbox prompt‑minimization framework that identifies the minimal subset of few‑shot prompts necessary for large language models (LLMs). In a case study, the framework reduces few‑shot exemplars by an average of 65.3% in character count while maintaining full propositional output fidelity, revealing that models tend to keep logical identifiers and constraint declarations while discarding natural language prose. The analysis further distinguishes between universal encoder and decoder models, offering insights into prompt compression and structural analysis.

FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations

arXiv:2606.02615v2 Announce Type: replace-cross Abstract: Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children's speech recognit...

A Dive into Vision-Language Models

FSA-GRPO: Teaching Auditory LLMs to Use Few-shot Demonstrations

arXiv:2606. 02615v1 Announce Type: cross Abstract: Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children's speech recognition.